/* Copyright 2007-2008 dnAnalytics Project.
 *
 * Contributors to this file:
 * Marcus Cuda
 *
 * This file is part of dnAnalytics.  dnAnalytics is licensed under the 
 * Microsoft Public License. See License.txt for a complete copy of the
 * license.
 */
using System;

namespace dnAnalytics.LinearAlgebra.Decomposition
{
    internal partial class DenseSvd : AbstractSvd
    {
        private double[] mMatrix;

        public DenseSvd(DenseMatrix matrix, bool computeVectors) : base(computeVectors)
        {
            mRows = matrix.Rows;
            mColumns = matrix.Columns;
            mMatrix = new double[mRows*mColumns];
            Buffer.BlockCopy(matrix.Data, 0, mMatrix, 0, mMatrix.Length*Constants.SizeOfDouble);
        }

        protected override void DoCompute()
        {
            int nm = System.Math.Min(mRows + 1, mColumns);
            mS = new DenseVector(nm);

            if (mComputeVectors)
            {
                mU = new DenseMatrix(mRows, mRows);
                mV = new DenseMatrix(mColumns, mColumns);
            }
            mConverged = Decompose();

            //adjust the size of s if row < columns.
            //we are using ported copy of linpack's svd code and it uses
            //a singular vector of length rows+1 when rows < columns.
            //the last element is not used and needs to be removed.
            //we should port lapack's svd routine to remove this problem.
            if (mRows < mColumns)
            {
                nm--;
                Vector tmp = new DenseVector(nm);
                for (int i = 0; i < nm; i++)
                {
                    tmp[i] = mS[i];
                }
                mS = tmp;
            }

            double eps = System.Math.Pow(2.0, -52.0);
            double tol = System.Math.Max(mRows, mColumns)*mS[0]*eps;
            mRank = 0;
            for (int h = 0; h < nm; h++)
            {
                if (mS[h] > tol)
                {
                    mRank++;
                }
            }

            //we no longer need the original matrix.
            mMatrix = null;
        }
    }
}